Is doing a master's in natural language processing combined with machine learning worth it?
The combination of NLP and ML is an interesting one. The most interesting work is not the mainstream systems employing feed forward, convolution kernel, and LSTM networks with word-to-vector input normalization or straight audio samples as input data.

The kinds that are most interesting are the kinds that more closely mimic the systems in which natural language developed, which are those of the language networks in the brain, including auditory, visual, and motor pathways and the kinds of cognitive abilities that support conversations beyond chat with no underlying comprehension or belief.

That some balanced rise and fall of beliefs and semantic associations are part of real conversation has been known for a few decades. The AI research directions along these lines include semantic nets, recursive artificial networks (an advanced type of RNN just entering the scene), and fuzzy belief models. Some of the current perspectives centered on letter, word, and grammar are obsolete from a linguistics point of view. Contemporary linguistics do not assume words are the elementary objects and formal grammar rules describe legal combinations of them. Smaller elements such as word roots, prefixes, suffixes, conjugations, phonetics, adjacency, and the dynamic aspects of language are more accurate modeling elements of real talk and writing.

Looking up some of these terms in an academic article search engine can pull up some of this research over the last two or three decades so that a historical background is clearer. Many of those articles have proposed systems, mathematics to describe the perspective, and sometimes algorithms and results of testing the modality. After spending a few days looking around, it will be easier to pick a master's thesis or a group of them to run past those at a university.

Applications for deeper forms of natural language capability beyond idiot talk chatbots is so profoundly needed, there will be little need to investigate the career options for most of these lines of research, provided the feasibility of developing components that fit into products and services is high for that line of research.